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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 9 of 9  /   
Committee Date Time Place Paper Title / Authors Abstract Paper #
MI 2024-03-04
14:00
Okinawa OKINAWAKEN SEINENKAIKAN
(Primary: On-site, Secondary: Online)
Computerized Classification Method for Molecular Subtypes of Low-grade Glioma on Brain MRI Images using Multi-scale 3D CNN with Channel and Spatial Attention Mechanism
Shimpei Kobayashi, Akiyoshi Hizukuri, Ryohei Nakayama (Ritsumeikan Univ.), Kaori Kusuda (THCU), Ken Masamune (TWMU), Yoshihiro Muragaki (Kobe Univ.) MI2023-79
The purpose of this study was to develop a computerized classification method for molecular subtypes of low-grade glioma... [more] MI2023-79
pp.156-157
MI 2024-03-04
14:24
Okinawa OKINAWAKEN SEINENKAIKAN
(Primary: On-site, Secondary: Online)
Attenuation Correction of Bone Scintigraphy Using pix2pix
Takaki Ojima, Ryohei Nakayama, Akiyoshi Hizukuri (Ritsumeikan Univ.), Yoya Tomita, Yasutaka Ichikawa, Hajime Sakuma (Mie Univ.) MI2023-81
The purpose of this study was to develop an attenuation correction method for bone SPECT images using 3D-pix2pix which i... [more] MI2023-81
pp.160-161
MI 2020-01-29
13:20
Okinawa OKINAWAKEN SEINENKAIKAN [Poster Presentation] Computerized Determination Method for Histological Classification of Breast Masses on Ultrasonographic Images Using CNN Features and Morphological Features
Shinya Kunieda, Akiyoshi Hizukuri, Ryohei Nakayama (Ritsumeikan Univ.) MI2019-76
The purpose of this study was to develop a computerized determination method for histological classifications of masses ... [more] MI2019-76
pp.53-55
MI 2020-01-29
13:20
Okinawa OKINAWAKEN SEINENKAIKAN [Poster Presentation] Computerized Classification Method of Benign and Malignant Masses in Multiple MRI Sequences using Convolutional Neural Network
Yuichi Mima, Akiyoshi Hizukuri, Ryohei Nakayama (Ritsumeikan Univer) MI2019-77
Breast magnetic resonance imaging (MRI) has a higher sensitivity of early breast cancer than mammography and ultrasonogr... [more] MI2019-77
pp.57-59
MI 2019-01-22
13:20
Okinawa   [Poster Presentation] Computerized Identification Method for Gene Expression on Pathological Images using Deep Learning
Akiyoshi Hizukuri, Ryohei Nakayama (Ritsumeikan Univ.), Kodama Yoshinori (Kyoto Pref. Univ. Med./Osaka national hospital), Masayuki Mano, Ema Yoshioka, Daisuke Kanematsu, Tomoko Shofuda, Yonehiro Kanemura (Osaka national hospital) MI2018-66
 [more] MI2018-66
pp.31-34
MI 2019-01-22
13:20
Okinawa   [Poster Presentation] Improvement of Image Quality of Ultra-Low Dose CT Images Using Different CNNs in Each of Lung Region and The Other Region
Motonari Sakurai, Ryohei Nakayama, Mitsuhiko Asao, Akiyoshi Hizukuri (Ritsumeikan Univ.), Yasutaka Ichikawa, Kakuya Kitagawa, Hazime Sakuma (Mie University) MI2018-70
 [more] MI2018-70
pp.41-44
MI 2019-01-22
13:20
Okinawa   [Poster Presentation] Identification of Organs from Pathological Specimen using Deep Learning
Norihiro Naito, Ryohei Nakayama, Akiyoshi Hizukuri (Ritsumeikan Univ), Mafumi Kurozumi, Toshiaki Manabe (Shiga Genaral Hospital) MI2018-71
 [more] MI2018-71
pp.45-48
MI 2014-01-26
13:30
Okinawa Bunka Tenbusu Kan Computerized Quantification Method for Architectural Distortion on Breast Ultrasonographic Image and Its Application to the Histological Classification for Breast mass
Akiyoshi Hizukuri, Ryohei Nakayama, Yumi Kashikura, Haruhiko Takase, Hiroharu Kawanaka, Tomoko Ogawa, Shinji Tsuruoka (Mie Univ.) MI2013-73
An architectural distortion is one of the important indications related to breast cancer on ultrasonographic images. It ... [more] MI2013-73
pp.91-95
PRMU, MI, IE 2011-05-20
15:10
Aichi   Computer aided diagnosis scheme for histological classification of breast mass on ultrasonic images
Akiyoshi Hizukuri, Ryohei Nakayama, Yumi Kashikura, Nobuo Nakako, Hiroharu Kawanaka, Haruhiko Takase, Tomoko Ogawa, Shinji Tsuruoka (Mie Univ.) IE2011-35 PRMU2011-27 MI2011-27
The purpose of this study is to develop a computer-aided diagnosis scheme for histological classifications of breast mas... [more] IE2011-35 PRMU2011-27 MI2011-27
pp.153-158
 Results 1 - 9 of 9  /   
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